Overview

Dataset statistics

Number of variables24
Number of observations3677
Missing cells7196
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory671.3 B

Variable types

Categorical13
Numeric11

Alerts

study room has constant value "0"Constant
servant room has constant value "0"Constant
store room has constant value "0"Constant
pooja room has constant value "0"Constant
others has constant value "0"Constant
society has a high cardinality: 674 distinct valuesHigh cardinality
sector has a high cardinality: 113 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2355 distinct valuesHigh cardinality
price is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 4 other fieldsHigh correlation
floorNum is highly overall correlated with property_typeHigh correlation
super_built_up_area is highly overall correlated with price and 6 other fieldsHigh correlation
built_up_area is highly overall correlated with price and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 3 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
society has 487 (13.2%) missing valuesMissing
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73095613)Skewed
built_up_area is highly skewed (γ1 = 40.70657243)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
floorNum has 129 (3.5%) zerosZeros
Wardrobe has 2268 (61.7%) zerosZeros
luxury_score has 463 (12.6%) zerosZeros

Reproduction

Analysis started2023-10-22 06:48:35.345151
Analysis finished2023-10-22 06:48:47.565377
Duration12.22 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Length

2023-10-22T12:18:47.613661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:47.687330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

society
Categorical

HIGH CARDINALITY  MISSING 

Distinct674
Distinct (%)21.1%
Missing487
Missing (%)13.2%
Memory size276.8 KiB
tulip violet
 
75
ss the leaf
 
73
dlf new town heights
 
42
shapoorji pallonji joyville gurugram
 
42
signature global park
 
35
Other values (669)
2923 

Length

Max length49
Median length38
Mean length17.756426
Min length1

Characters and Unicode

Total characters56643
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)9.6%

Sample

1st rowpareena mi casa
2nd rowtata primanti
3rd rowm3m capital
4th rowsare crescent parc
5th rowla vida by tata housing

Common Values

ValueCountFrequency (%)
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
dlf new town heights 42
 
1.1%
shapoorji pallonji joyville gurugram 42
 
1.1%
signature global park 35
 
1.0%
shree vardhman victoria 34
 
0.9%
smart world orchard 32
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
dlf the ultima 31
 
0.8%
paras dews 31
 
0.8%
Other values (664) 2763
75.1%
(Missing) 487
 
13.2%

Length

2023-10-22T12:18:47.762899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 350
 
3.8%
dlf 220
 
2.4%
park 209
 
2.3%
city 166
 
1.8%
emaar 155
 
1.7%
global 153
 
1.7%
m3m 152
 
1.7%
signature 150
 
1.6%
heights 134
 
1.5%
godrej 114
 
1.2%
Other values (781) 7388
80.4%

Most occurring characters

ValueCountFrequency (%)
6003
 
10.6%
a 5864
 
10.4%
e 5243
 
9.3%
r 4171
 
7.4%
s 3472
 
6.1%
i 3341
 
5.9%
t 3230
 
5.7%
l 2943
 
5.2%
o 2755
 
4.9%
n 2702
 
4.8%
Other values (31) 16919
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50095
88.4%
Space Separator 6003
 
10.6%
Decimal Number 527
 
0.9%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5864
11.7%
e 5243
 
10.5%
r 4171
 
8.3%
s 3472
 
6.9%
i 3341
 
6.7%
t 3230
 
6.4%
l 2943
 
5.9%
o 2755
 
5.5%
n 2702
 
5.4%
h 2100
 
4.2%
Other values (16) 14274
28.5%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50095
88.4%
Common 6548
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5864
11.7%
e 5243
 
10.5%
r 4171
 
8.3%
s 3472
 
6.9%
i 3341
 
6.7%
t 3230
 
6.4%
l 2943
 
5.9%
o 2755
 
5.5%
n 2702
 
5.4%
h 2100
 
4.2%
Other values (16) 14274
28.5%
Common
ValueCountFrequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56643
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6003
 
10.6%
a 5864
 
10.4%
e 5243
 
9.3%
r 4171
 
7.4%
s 3472
 
6.1%
i 3341
 
5.9%
t 3230
 
5.7%
l 2943
 
5.2%
o 2755
 
4.9%
n 2702
 
4.8%
Other values (31) 16919
29.9%

sector
Categorical

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
sohna road
 
154
sector 85
 
108
sector 102
 
107
sector 92
 
100
sector 69
 
93
Other values (108)
3115 

Length

Max length26
Median length9
Mean length9.3209138
Min length7

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 68
2nd rowsector 72
3rd rowsector 113
4th rowsector 92
5th rowsector 113

Common Values

ValueCountFrequency (%)
sohna road 154
 
4.2%
sector 85 108
 
2.9%
sector 102 107
 
2.9%
sector 92 100
 
2.7%
sector 69 93
 
2.5%
sector 90 89
 
2.4%
sector 81 87
 
2.4%
sector 65 87
 
2.4%
sector 109 86
 
2.3%
sector 79 76
 
2.1%
Other values (103) 2690
73.2%

Length

2023-10-22T12:18:47.845877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3452
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (106) 2915
39.5%

Most occurring characters

ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23299
68.0%
Decimal Number 7269
 
21.2%
Space Separator 3705
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1076
14.8%
0 804
11.1%
8 780
10.7%
9 764
10.5%
6 742
10.2%
7 684
9.4%
2 676
9.3%
3 666
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
ValueCountFrequency (%)
3705
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23299
68.0%
Common 10974
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
ValueCountFrequency (%)
3705
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 764
 
7.0%
6 742
 
6.8%
7 684
 
6.2%
2 676
 
6.2%
3 666
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:47.929852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2023-10-22T12:18:48.019338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:48.116609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2023-10-22T12:18:48.209878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:48.466912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2023-10-22T12:18:48.554322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
88.8%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%

areaWithType
Categorical

Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.2 KiB
Plot area 360(301.01 sq.m.)
 
37
Plot area 300(250.84 sq.m.)
 
26
Plot area 200(167.23 sq.m.)
 
19
Plot area 502(419.74 sq.m.)
 
19
Plot area 270(225.75 sq.m.)
 
17
Other values (2350)
3559 

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 1245(115.66 sq.m.)
2nd rowSuper Built up area 2905(269.88 sq.m.)Carpet area: 2495 sq.ft. (231.79 sq.m.)
3rd rowSuper Built up area 1665(154.68 sq.m.)
4th rowSuper Built up area 1750(162.58 sq.m.)
5th rowSuper Built up area 2690(249.91 sq.m.)Built Up area: 2350 sq.ft. (218.32 sq.m.)Carpet area: 2170 sq.ft. (201.6 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 37
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Plot area 502(419.74 sq.m.) 19
 
0.5%
Plot area 270(225.75 sq.m.) 17
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Plot area 150(125.42 sq.m.) 14
 
0.4%
Super Built up area 1650(153.29 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.) 14
 
0.4%
Other values (2345) 3482
94.7%

Length

2023-10-22T12:18:48.650619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Close Punctuation 5535
 
2.8%
Open Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
) 5535
 
5.1%
( 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:48.730192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2023-10-22T12:18:48.798299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:48.872773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2023-10-22T12:18:48.940386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3187381
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row2
4th row2
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2023-10-22T12:18:49.015318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:49.090671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7966102
Minimum-1
Maximum51
Zeros129
Zeros (%)3.5%
Negative3
Negative (%)0.1%
Memory size57.5 KiB
2023-10-22T12:18:49.166858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range52
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0143088
Coefficient of variation (CV)0.88489831
Kurtosis4.509584
Mean6.7966102
Median Absolute Deviation (MAD)3
Skewness1.6918973
Sum24862
Variance36.171911
MonotonicityNot monotonic
2023-10-22T12:18:49.255430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 348
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (34) 940
25.6%
ValueCountFrequency (%)
-1 3
 
0.1%
0 129
 
3.5%
1 348
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size225.5 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowEast
4th rowNorth-East
5th rowNorth-East

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2023-10-22T12:18:49.339431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:49.423603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.5 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.385912
Min length9

Characters and Unicode

Total characters49220
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder Construction
2nd rowRelatively New
3rd rowUnder Construction
4th rowModerately Old
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2023-10-22T12:18:49.502387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:49.580837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.4%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8431
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2307
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14065
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38801
78.8%
Uppercase Letter 7048
 
14.3%
Space Separator 3371
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8431
21.7%
l 4721
12.2%
t 3637
9.4%
y 3105
 
8.0%
r 2887
 
7.4%
d 2307
 
5.9%
w 2239
 
5.8%
i 2218
 
5.7%
a 2209
 
5.7%
o 1991
 
5.1%
Other values (7) 5056
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.4%
P 896
12.7%
O 866
 
12.3%
U 572
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45849
93.2%
Common 3371
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8431
18.4%
l 4721
 
10.3%
t 3637
 
7.9%
y 3105
 
6.8%
r 2887
 
6.3%
d 2307
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2218
 
4.8%
a 2209
 
4.8%
Other values (14) 11856
25.9%
Common
ValueCountFrequency (%)
3371
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8431
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2307
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14065
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:49.671634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2023-10-22T12:18:49.762131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:49.852231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2023-10-22T12:18:49.940651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1987
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:50.044283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2023-10-22T12:18:50.278822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3677
100.0%

Length

2023-10-22T12:18:50.357590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:50.423877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3677
100.0%

servant room
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3677
100.0%

Length

2023-10-22T12:18:50.478661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:50.545365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3677
100.0%

store room
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3677
100.0%

Length

2023-10-22T12:18:50.600210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:50.666818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3677
100.0%

pooja room
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3677
100.0%

Length

2023-10-22T12:18:50.722076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:50.788304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3677
100.0%

others
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3677
100.0%

Length

2023-10-22T12:18:50.842855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:50.908825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3677
100.0%

Wardrobe
Real number (ℝ)

Distinct26
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4185477
Minimum0
Maximum36
Zeros2268
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:50.965147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile5
Maximum36
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6413704
Coefficient of variation (CV)1.8620243
Kurtosis26.770283
Mean1.4185477
Median Absolute Deviation (MAD)0
Skewness3.9598574
Sum5216
Variance6.9768376
MonotonicityNot monotonic
2023-10-22T12:18:51.035309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2268
61.7%
3 428
 
11.6%
4 244
 
6.6%
1 229
 
6.2%
2 220
 
6.0%
5 108
 
2.9%
6 55
 
1.5%
7 30
 
0.8%
9 20
 
0.5%
10 16
 
0.4%
Other values (16) 59
 
1.6%
ValueCountFrequency (%)
0 2268
61.7%
1 229
 
6.2%
2 220
 
6.0%
3 428
 
11.6%
4 244
 
6.6%
5 108
 
2.9%
6 55
 
1.5%
7 30
 
0.8%
8 12
 
0.3%
9 20
 
0.5%
ValueCountFrequency (%)
36 1
 
< 0.1%
28 1
 
< 0.1%
26 1
 
< 0.1%
24 3
0.1%
22 2
 
0.1%
21 2
 
0.1%
20 4
0.1%
19 1
 
< 0.1%
18 2
 
0.1%
16 5
0.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2421 
2
1046 
1
 
210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

Length

2023-10-22T12:18:51.109267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:18:51.179080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2421
65.8%
2 1046
28.4%
1 210
 
5.7%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.484906
Minimum0
Maximum174
Zeros463
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-10-22T12:18:51.250359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.069642
Coefficient of variation (CV)0.74238948
Kurtosis-0.88005839
Mean71.484906
Median Absolute Deviation (MAD)38
Skewness0.45963384
Sum262850
Variance2816.3869
MonotonicityNot monotonic
2023-10-22T12:18:51.337539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2312
62.9%
ValueCountFrequency (%)
0 463
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2023-10-22T12:18:46.003530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:36.883303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.818956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.716446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.677181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.581685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.602655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.438690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.269441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.261149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.117679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.222732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:36.968206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.904377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.790032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.758564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.662902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.677783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.511686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.348180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.336294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.196110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.301712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.050311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.985485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.865491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.841965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.744987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.753849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.588936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.426677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.413648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.277867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.377455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.128590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.061510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.936155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.919890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.820021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.824326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.663037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.500959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.492044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.353440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.463313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.215489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.146509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.015720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.007313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.903141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.904393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.745288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.584148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.571770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.440078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.549848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.304058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.231066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.221636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.094889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.986639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.985242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.821001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.669252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.652128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.526151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.629212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.386071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.307983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.293764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.172866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.063633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.059788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.892419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.884213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.725222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.605211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.711445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.468972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.386767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.367682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.251186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.137771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.133183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.967170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.950488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.805333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.683316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.793774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.556869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.470854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.445832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.334108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.219018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.212503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.036712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.030789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.877055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.767016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.872382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.643942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.552289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.523536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.412984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.295975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.286584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.115515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.100978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.957676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.843735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:46.951944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:37.731661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:38.633781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:39.599110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:40.494872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:41.377188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:42.362857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:43.192603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:44.180387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.036638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-10-22T12:18:45.924206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-10-22T12:18:51.424447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_areaWardrobeluxury_scoreproperty_typebalconyfacingagePossessionfurnishing_type
price1.0000.7440.7440.6810.7200.0010.7720.6050.6130.2810.2160.5430.1360.0210.1020.175
price_per_sqft0.7441.0000.2070.4170.411-0.1260.2870.1320.1360.2380.0540.2010.0330.0000.0560.022
area0.7440.2071.0000.6240.6870.1160.9480.8350.8010.1780.2600.0280.0110.0220.0000.042
bedRoom0.6810.4170.6241.0000.862-0.1040.8000.3800.5690.2380.0580.5950.1760.0320.1300.167
bathroom0.7200.4110.6870.8621.000-0.0040.8190.4650.5990.2570.1800.4720.2250.0440.1110.197
floorNum0.001-0.1260.116-0.104-0.0041.0000.1520.0920.159-0.0190.2320.5240.0880.0000.1320.025
super_built_up_area0.7720.2870.9480.8000.8190.1521.0000.9260.8940.1400.2221.0000.3060.0000.0860.132
built_up_area0.6050.1320.8350.3800.4650.0920.9261.0000.9690.1500.2900.0000.0001.0000.0000.087
carpet_area0.6130.1360.8010.5690.5990.1590.8940.9691.0000.1590.2400.0000.0260.0000.0000.000
Wardrobe0.2810.2380.1780.2380.257-0.0190.1400.1500.1591.0000.1940.3170.1330.0380.0980.375
luxury_score0.2160.0540.2600.0580.1800.2320.2220.2900.2400.1941.0000.3280.2240.0650.2550.243
property_type0.5430.2010.0280.5950.4720.5241.0000.0000.0000.3170.3281.0000.2140.0940.3790.083
balcony0.1360.0330.0110.1760.2250.0880.3060.0000.0260.1330.2240.2141.0000.0160.2740.176
facing0.0210.0000.0220.0320.0440.0000.0001.0000.0000.0380.0650.0940.0161.0000.0920.046
agePossession0.1020.0560.0000.1300.1110.1320.0860.0000.0000.0980.2550.3790.2740.0921.0000.214
furnishing_type0.1750.0220.0420.1670.1970.0250.1320.0870.0000.3750.2430.0830.1760.0460.2141.000

Missing values

2023-10-22T12:18:47.089779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T12:18:47.311574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T12:18:47.466397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersWardrobefurnishing_typeluxury_score
0flatpareena mi casasector 680.927389.01245.0Super Built up area 1245(115.66 sq.m.)2235.0WestUnder Construction1245.0NaNNaN000000050
1flattata primantisector 724.0016032.02495.0Super Built up area 2905(269.88 sq.m.)Carpet area: 2495 sq.ft. (231.79 sq.m.)453+22.0WestRelatively New2905.0NaN2495.00000002174
2flatm3m capitalsector 1132.0012012.01665.0Super Built up area 1665(154.68 sq.m.)33224.0NaNUnder Construction1665.0NaNNaN000000047
3flatsare crescent parcsector 920.874971.01750.0Super Built up area 1750(162.58 sq.m.)4422.0EastModerately Old1750.0NaNNaN0000041115
4flatla vida by tata housingsector 1132.7810334.02690.0Super Built up area 2690(249.91 sq.m.)Built Up area: 2350 sq.ft. (218.32 sq.m.)Carpet area: 2170 sq.ft. (201.6 sq.m.)343+5.0North-EastRelatively New2690.02350.02170.00000000174
5flatshapoorji pallonji joyville gurugramsector 1021.9510529.01852.0Super Built up area 1852(172.06 sq.m.)33310.0North-EastRelatively New1852.0NaNNaN000000059
6houseunitech espacesector 507.4234351.02160.0Plot area 2160(200.67 sq.m.)Built Up area: 3200 sq.ft. (297.29 sq.m.)4432.0WestOld PropertyNaN3200.0NaN000004288
7flatdlf the belairesector 5410.0024557.04072.0Super Built up area 4072(378.3 sq.m.)Built Up area: 3000 sq.ft. (278.71 sq.m.)Carpet area: 2800 sq.ft. (260.13 sq.m.)453+17.0NorthModerately Old4072.03000.02800.00000041167
8flatss the leafsector 851.096661.01636.0Super Built up area 1640(152.36 sq.m.)Built Up area: 1475 sq.ft. (137.03 sq.m.)Carpet area: 1350 sq.ft. (125.42 sq.m.)2235.0North-EastRelatively New1640.01475.01350.0000000049
9houseNaNsector 40.9215257.0603.0Plot area 67(56.02 sq.m.)4434.0EastModerately OldNaN603.0NaN00000300
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersWardrobefurnishing_typeluxury_score
3793flatconscient elevatesector 593.9016993.02295.0Built Up area: 2295 (213.21 sq.m.)3100.0NaNUndefinedNaN2295.0NaN00000000
3794houseNaNsector 71.5513889.01116.0Plot area 120(100.34 sq.m.)2221.0EastModerately OldNaN1080.0NaN000002252
3795flatsignature andour heightssector 710.558607.0639.0Super Built up area 639(59.37 sq.m.)Built Up area: 600 sq.ft. (55.74 sq.m.)Carpet area: 541 sq.ft. (50.26 sq.m.)2217.0South-EastRelatively New639.0600.0541.0000002224
3796housearjun marg/ sector- 26 phase- 1/ golf course roadsector 2631.5035000.09000.0Plot area 1000(836.13 sq.m.)793+3.0North-EastModerately OldNaN9000.0NaN000009174
3797flattata primantisector 724.0013769.02905.0Super Built up area 2905(269.88 sq.m.)4527.0South-EastModerately Old2905.0NaNNaN000000038
3798houseNaNsector 280.7520833.0360.0Plot area 40(33.45 sq.m.)743+4.0NaNModerately OldNaN360.0NaN00000007
3799flatshree vardhman victoriasector 701.809230.01950.0Super Built up area 1950(181.16 sq.m.)333+10.0North-EastNew Property1950.0NaNNaN000004249
3800flatats tourmalinesector 1091.456744.02150.0Super Built up area 2150(199.74 sq.m.)Built Up area: 1797 sq.ft. (166.95 sq.m.)Carpet area: 1660 sq.ft. (154.22 sq.m.)343+4.0North-WestRelatively New2150.01797.01660.0000000071
3801flatdlf the belairesector 547.0023333.03000.0Super Built up area 3000(278.71 sq.m.)463+12.0EastModerately Old3000.0NaNNaN0000062108
3802houseraj villassector 528.0025543.03132.0Carpet area: 348 (290.97 sq.m.)653+4.0EastUndefinedNaNNaN348.000000000